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ActivityNet: A large-scale video benchmark for human activity understanding
TLDR
This paper introduces ActivityNet, a new large-scale video benchmark for human activity understanding that aims at covering a wide range of complex human activities that are of interest to people in their daily living. Expand
A Benchmark and Simulator for UAV Tracking
TLDR
A new aerial video dataset and benchmark for low altitude UAV target tracking, as well as, a photo-realistic UAV simulator that can be coupled with tracking methods to easily extend existing real-world datasets. Expand
The Visual Object Tracking VOT2016 Challenge Results
TLDR
The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment. Expand
Robust visual tracking via multi-task sparse learning
TLDR
Experimental results show that MTT methods consistently outperform state-of-the-art trackers and mining the interdependencies between particles improves tracking performance and overall computational complexity. Expand
ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing
TLDR
This paper proposes a novel structured deep network, dubbed ISTA-Net, which is inspired by the Iterative Shrinkage-Thresholding Algorithm (ISTA) for optimizing a general $$ norm CS reconstruction model and develops an effective strategy to solve the proximal mapping associated with the sparsity-inducing regularizer using nonlinear transforms. Expand
Context-Aware Correlation Filter Tracking
TLDR
This paper reformulate the original optimization problem and provides a closed form solution for single and multi-dimensional features in the primal and dual domain and significantly improves the performance of many CF trackers with only a modest impact on frame rate. Expand
TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild
TLDR
This work presents TrackingNet, the first large-scale dataset and benchmark for object tracking in the wild, which covers a wide selection of object classes in broad and diverse context and provides an extensive benchmark on TrackingNet by evaluating more than 20 trackers. Expand
DAPs: Deep Action Proposals for Action Understanding
TLDR
Deep Action Proposals (DAPs), an effective and efficient algorithm for generating temporal action proposals from long videos, is introduced, which outperforms previous work on a large scale action benchmark, runs at 134 FPS making it practical for large-scale scenarios, and exhibits an appealing ability to generalize. Expand
Maximum Margin Distance Learning for Dynamic Texture Recognition
TLDR
This paper proposes an efficient maximum margin distance learning (MMDL) method, called DL-PEGASOS, which outperforms state-of-the-art recognition methods on the UCLA benchmark DT dataset and shows that, for certain classes of DTs, spatial texture features are dominantly "salient", while for other classes, this "saliency" lies in their temporal features. Expand
DeepGCNs: Can GCNs Go As Deep As CNNs?
TLDR
This work presents new ways to successfully train very deep GCNs by borrowing concepts from CNNs, specifically residual/dense connections and dilated convolutions, and adapting them to GCN architectures, and building a very deep 56-layer GCN. Expand
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